Disentanglement representation extraction aims to figure out explanatory factors of the input data to generate more meaningful features. Early attempts including bilinear model [1]}, autoencoder [2]}, manifold learning [3]}, Restricted Boltzmann Machine [4]} and so on. Recently, Variational Autoencoder (VAE) and Generative Adversarial Networks (GANs) are found to be more powerful. Besides, researchers are also interested in explorations about the ability of disentanglement representation to solve challenge tasks [5]}, [6]}, [7]}.
